Machine Learning based on Computational Fluid Dynamics enables geometric design optimisation of the NeoVAD blades

Machine Learning based on Computational Fluid Dynamics enables geometric design optimisation of the NeoVAD blades | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Machine Learning based on Computational Fluid Dynamics enables geometric design optimisation of the NeoVAD blades Lee Nissim, Shweta Karnik, Peter Alex Smith, Yaxin Wang, Oscar Howard Frazier, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2405001/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 May, 2023 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract The NeoVAD is a proposed paediatric axial-flow Left Ventricular Assist Device (LVAD), small enough to be implanted in infants. The design of the impeller and diffuser blades is important for hydrodynamic performance and haemocompatibility of the pump. This study aimed to optimise the blades for pump efficiency using Computational Fluid Dynamics (CFD), machine learning and global optimisation. Meshing of each design typically included ~6 million hexahedral elements and a Shear Stress Transport (SST) turbulence model was used to close the Reynolds Averaged Navier-Stokes (RANS) equations. CFD models of 32 base geometries, operating at 8 flow rates between 0.5 and 4 l/min, were created to match experimental studies. These were validated by comparison of the pressure-flow and efficiency-flow curves with those experimentally measured for all base prototype pumps. A surrogate model was required to allow the optimisation routine to conduct an efficient search; a multi-linear regression, Gaussian Process Regression and a Bayesian Regularised Artificial Neural Network (BRANN) predicted the optimisation objective at design points not explicitly simulated. A Genetic Algorithm was used to search for an optimal design. The optimised design offered a 5.51% increase in efficiency at design point (a 20.9% performance increase) as compared to the best performing pump from the 32 base designs.An optimisation method for the blade design of LVADs has been shown to work for a single objective function and future work will consider multi-objective optimisation. Health sciences/Cardiology/Cardiac device therapy Physical sciences/Physics/Fluid dynamics Physical sciences/Engineering/Biomedical engineering Physical sciences/Engineering/Mechanical engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 May, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 02 Feb, 2023 Reviews received at journal 12 Jan, 2023 Reviewers agreed at journal 08 Jan, 2023 Reviewers invited by journal 07 Jan, 2023 Editor assigned by journal 02 Jan, 2023 Editor invited by journal 02 Jan, 2023 Submission checks completed at journal 02 Jan, 2023 First submitted to journal 22 Dec, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2405001","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":164297446,"identity":"5c3ac0a4-e9af-4ea7-a4db-463a99477655","order_by":0,"name":"Lee Nissim","email":"","orcid":"","institution":"University of Bath","correspondingAuthor":false,"prefix":"","firstName":"Lee","middleName":"","lastName":"Nissim","suffix":""},{"id":164297447,"identity":"1f3391de-da2a-4c5e-ba26-c3985f61989c","order_by":1,"name":"Shweta Karnik","email":"","orcid":"","institution":"Texas Heart Institute","correspondingAuthor":false,"prefix":"","firstName":"Shweta","middleName":"","lastName":"Karnik","suffix":""},{"id":164297448,"identity":"1715cf6c-2fdb-4e54-a345-13a8a09557ff","order_by":2,"name":"Peter Alex Smith","email":"","orcid":"","institution":"Texas Heart Institute","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"Alex","lastName":"Smith","suffix":""},{"id":164297450,"identity":"502f26cf-23ae-4e24-a54b-cdcc45b6a550","order_by":3,"name":"Yaxin Wang","email":"","orcid":"","institution":"Texas Heart Institute","correspondingAuthor":false,"prefix":"","firstName":"Yaxin","middleName":"","lastName":"Wang","suffix":""},{"id":164297452,"identity":"c24e1e81-f9c6-4d4d-8d1b-8f7424d5b836","order_by":4,"name":"Oscar Howard Frazier","email":"","orcid":"","institution":"Texas Heart Institute","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"Howard","lastName":"Frazier","suffix":""},{"id":164297455,"identity":"1052576c-8b74-48c1-9ef9-df5995e188b3","order_by":5,"name":"Katharine Fraser","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACCQYeBoYKMJP5GJA4AGYyNhDScgbMZEtjYEggTQuPGXFaJBt4DzAc3GNnzy995tuDnz/uMPC3H2CTnIFHizQDXwLDgWfJiTP7crcb9iQ8Y5A4k8AmuQGPFjkGHgPmDweYEwzO8G6T4Ek4zMBwg4FN8gEBLQwHDtTb25/heSb5B6hFnpAWaYiWw4wbeHjYpEG2GIC04HOYZDOPwYEDB44nzjjDZm4sk3aYx/BMYrMlPu9LHO8xfHDgQLU9fw/zs4dvbA7LyR0/fPBmDx4tDMywGIcCHgKxMgpGwSgYBaOAGAAA4rBLxH7c1CMAAAAASUVORK5CYII=","orcid":"","institution":"University of Bath","correspondingAuthor":true,"prefix":"","firstName":"Katharine","middleName":"","lastName":"Fraser","suffix":""}],"badges":[],"createdAt":"2022-12-22 11:59:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2405001/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2405001/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-33708-9","type":"published","date":"2023-05-03T20:43:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44729898,"identity":"c5fc30a5-bce1-4056-813c-89060d2b6af6","added_by":"auto","created_at":"2023-10-16 21:22:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2736411,"visible":true,"origin":"","legend":"","description":"","filename":"TechnicalCheckAmendments.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2405001/v1_covered_db08dd8d-a7d6-49e4-9f58-d678f19a6cfe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning based on Computational Fluid Dynamics enables geometric design optimisation of the NeoVAD blades","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2405001/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2405001/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The NeoVAD is a proposed paediatric axial-flow Left Ventricular Assist Device (LVAD), small enough to be implanted in infants. 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